{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "amended-composer",
   "metadata": {},
   "source": [
    "框架\n",
    "\n",
    "    一、导入数据\n",
    "    二、原始序列的检验\n",
    "    三、一阶差分序列的检验\n",
    "    四、定阶（参数调优)\n",
    "    五、建模与预测\n",
    "    \n",
    "    参考材料：《Python数据分析与挖掘实践》"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-measurement",
   "metadata": {},
   "source": [
    "## 工具准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "short-partner",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from matplotlib.pylab import style #自定义图表风格\n",
    "style.use('ggplot')\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "# 解决坐标轴刻度负号乱码\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "#pip install statsmodels\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf  #自相关图、偏自相关图\n",
    "from statsmodels.tsa.stattools import adfuller as ADF #平稳性检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox #白噪声检验\n",
    "import statsmodels.api as sm #D-W检验,一阶自相关检验\n",
    "from statsmodels.graphics.api import qqplot #画QQ图,检验一组数据是否服从正态分布\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-metabolism",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "israeli-ballot",
   "metadata": {},
   "outputs": [],
   "source": [
    "yearAndMonth = [\n",
    "    \"2019/10\",\n",
    "    \"2019/11\",\n",
    "    \"2019/12\",\n",
    "    \"2020/1\",\n",
    "    \"2020/2\",\n",
    "    \"2020/3\",\n",
    "    \"2020/4\",\n",
    "    \"2020/5\",\n",
    "    \"2020/6\",\n",
    "    \"2020/7\",\n",
    "    \"2020/8\",\n",
    "    \"2020/9\",\n",
    "    \"2020/10\",\n",
    "    \"2020/11\",\n",
    "    \"2020/12\",\n",
    "    \"2021/1\",\n",
    "    \"2021/2\",\n",
    "    \"2021/3\",\n",
    "    \"2021/4\",\n",
    "    \"2021/5\",\n",
    "    \"2021/6\",\n",
    "    \"2021/7\",\n",
    "    \"2021/8\",\n",
    "    \"2021/9\"\n",
    "]\n",
    "\n",
    "salesData = [\n",
    "\"1.21\",\n",
    "\"1.12\",\n",
    "\"1.00\",\n",
    "\"1.11\",\n",
    "\"0.30\",\n",
    "\"0.64\",\n",
    "\"0.77\",\n",
    "\"0.63\",\n",
    "\"0.65\",\n",
    "\"0.63\",\n",
    "\"0.71\",\n",
    "\"0.91\",\n",
    "\"0.84\",\n",
    "\"0.94\",\n",
    "\"1.50\",\n",
    "\"1.15\",\n",
    "\"0.79\",\n",
    "\"1.23\",\n",
    "\"1.00\",\n",
    "\"0.90\",\n",
    "\"0.73\",\n",
    "\"1.05\",\n",
    "\"0.86\",\n",
    "\"1.45\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "sonic-breakdown",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 24 entries, 2019/10 to 2021/9\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   sale    24 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 384.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019/10</th>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019/11</th>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019/12</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/1</th>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/2</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/3</th>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/4</th>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/5</th>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/6</th>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/7</th>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/8</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/9</th>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/10</th>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/11</th>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/12</th>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/1</th>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/2</th>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/3</th>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/4</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/5</th>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/6</th>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/7</th>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/8</th>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/9</th>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sale\n",
       "date         \n",
       "2019/10  1.21\n",
       "2019/11  1.12\n",
       "2019/12  1.00\n",
       "2020/1   1.11\n",
       "2020/2   0.30\n",
       "2020/3   0.64\n",
       "2020/4   0.77\n",
       "2020/5   0.63\n",
       "2020/6   0.65\n",
       "2020/7   0.63\n",
       "2020/8   0.71\n",
       "2020/9   0.91\n",
       "2020/10  0.84\n",
       "2020/11  0.94\n",
       "2020/12  1.50\n",
       "2021/1   1.15\n",
       "2021/2   0.79\n",
       "2021/3   1.23\n",
       "2021/4   1.00\n",
       "2021/5   0.90\n",
       "2021/6   0.73\n",
       "2021/7   1.05\n",
       "2021/8   0.86\n",
       "2021/9   1.45"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转换数据类型\n",
    "index = pd.DatetimeIndex(yearAndMonth)\n",
    "data = {\"date\":yearAndMonth,\"sale\":salesData}\n",
    "\n",
    "sale = pd.DataFrame(data)\n",
    "# 转换数据类型\n",
    "sale.set_index(['date'],inplace=True)\n",
    "sale.sale=sale.sale.astype('float')\n",
    "\n",
    "sale.info()\n",
    "sale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-assistant",
   "metadata": {},
   "source": [
    "## 原始序列的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "unsigned-photography",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-1.9453299234533037, 0.3110459399342529, 9, 14, {'1%': -4.01203360058309, '5%': -3.1041838775510207, '10%': -2.6909873469387753}, 3.0378608846157746)\n",
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  2.016065   0.155642\n"
     ]
    }
   ],
   "source": [
    "sale.plot()\n",
    "plt.show()\n",
    "print('原始序列的ADF检验结果为：',ADF(sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "accomplished-aggregate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-1.5468911977035305, 0.5102007082115626, 9, 13, {'1%': -4.068853732362312, '5%': -3.1271488757396453, '10%': -2.7017297633136095}, 8.005346249740633)\n",
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  3.408824   0.064849\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d1_sale=sale.diff(periods=1, axis=0).dropna()\n",
    "\n",
    "#时序图\n",
    "plt.figure(figsize=(10,5))\n",
    "d1_sale.plot()\n",
    "plt.show()\n",
    "\n",
    "plot_acf(d1_sale,lags=22).show()\n",
    "plot_pacf(d1_sale,lags=10).show()\n",
    "print('原始序列的ADF检验结果为：',ADF(d1_sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d1_sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "single-carroll",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-5.847789995960699, 3.648953535681926e-07, 2, 19, {'1%': -3.8326031418574136, '5%': -3.0312271701414204, '10%': -2.655519584487535}, 13.5872141336291)\n",
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  8.003312   0.004669\n"
     ]
    }
   ],
   "source": [
    "d2_sale=d1_sale.diff(periods=1, axis=0).dropna()\n",
    "\n",
    "#时序图\n",
    "plt.figure(figsize=(10,5))\n",
    "d2_sale.plot()\n",
    "plt.show()\n",
    "\n",
    "# plot_acf(d2_sale,lags=9).show()\n",
    "# plot_pacf(d2_sale,lags=4).show()\n",
    "print('原始序列的ADF检验结果为：',ADF(d2_sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d2_sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "affected-majority",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-3.866724125798769, 0.002293855123295211, 4, 16, {'1%': -3.9240193847656246, '5%': -3.0684982031250003, '10%': -2.67389265625}, 17.215140775680958)\n",
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  9.664108   0.001879\n"
     ]
    }
   ],
   "source": [
    "d3_sale=d2_sale.diff(periods=1, axis=0).dropna()\n",
    "\n",
    "#时序图\n",
    "plt.figure(figsize=(10,5))\n",
    "d3_sale.plot()\n",
    "plt.show()\n",
    "\n",
    "# plot_acf(d3_sale,lags=8).show()\n",
    "# plot_pacf(d3_sale,lags=3).show()\n",
    "print('原始序列的ADF检验结果为：',ADF(d3_sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d3_sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gothic-litigation",
   "metadata": {},
   "source": [
    "平稳性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "random-balance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-1.5468911977035305, 0.5102007082115626, 9, 13, {'1%': -4.068853732362312, '5%': -3.1271488757396453, '10%': -2.7017297633136095}, 8.005346249740633)\n"
     ]
    }
   ],
   "source": [
    "#方法：单位根检验\n",
    "\n",
    "print('原始序列的ADF检验结果为：',ADF(d1_sale.sale))\n",
    "\n",
    "# 解读：P值大于显著性水平α（0.05），接受原假设（非平稳序列），说明原始序列是非平稳序列。\n",
    "# 也可以判断t值（第一个值），如果小于第五个值，小于1%的值=严格拒绝原假设，小于百分五的值=拒绝原假设，依次递减"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composite-savage",
   "metadata": {},
   "source": [
    "白噪声检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "given-ceremony",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  3.408824   0.064849\n"
     ]
    }
   ],
   "source": [
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d1_sale,lags=1))#返回统计量、P值\n",
    "\n",
    "#解读：p值小于0.05，拒绝原假设（纯随机序列），说明一阶差分序列是非白噪声。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "sticky-steam",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "(p, q) =(sm.tsa.arma_order_select_ic(sale,max_ar=3,max_ma=3,ic='aic')['aic_min_order'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "sweet-static",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(p,q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "overall-brunswick",
   "metadata": {},
   "source": [
    "### 五、建模及预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "serious-endorsement",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "#创建模型\n",
    "model=ARIMA(sale,order=(p,0,q)).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "comprehensive-cradle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>sale</td>       <th>  No. Observations:  </th>   <td>24</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-1.772</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 19 Nov 2021</td> <th>  AIC                </th>  <td>9.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>12:52:58</td>     <th>  BIC                </th> <td>13.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-2019</td>    <th>  HQIC               </th> <td>10.482</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 09-01-2021</td>   <th>                     </th>    <td> </td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.9378</td> <td>    0.078</td> <td>   12.061</td> <td> 0.000</td> <td>    0.785</td> <td>    1.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.3302</td> <td>    0.445</td> <td>    0.742</td> <td> 0.458</td> <td>   -0.543</td> <td>    1.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0675</td> <td>    0.020</td> <td>    3.373</td> <td> 0.001</td> <td>    0.028</td> <td>    0.107</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.04</td> <th>  Jarque-Bera (JB):  </th> <td>1.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.84</td> <th>  Prob(JB):          </th> <td>0.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.83</td> <th>  Skew:              </th> <td>0.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.79</td> <th>  Kurtosis:          </th> <td>4.09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                   sale   No. Observations:                   24\n",
       "Model:                 ARIMA(1, 0, 0)   Log Likelihood                  -1.772\n",
       "Date:                Fri, 19 Nov 2021   AIC                              9.544\n",
       "Time:                        12:52:58   BIC                             13.079\n",
       "Sample:                    10-01-2019   HQIC                            10.482\n",
       "                         - 09-01-2021                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.9378      0.078     12.061      0.000       0.785       1.090\n",
       "ar.L1          0.3302      0.445      0.742      0.458      -0.543       1.203\n",
       "sigma2         0.0675      0.020      3.373      0.001       0.028       0.107\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):                 1.21\n",
       "Prob(Q):                              0.84   Prob(JB):                         0.55\n",
       "Heteroskedasticity (H):               0.83   Skew:                             0.07\n",
       "Prob(H) (two-sided):                  0.79   Kurtosis:                         4.09\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看模型报告\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "infrared-silly",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未来7月的销量预测：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2021-10-01    1.106916\n",
       "2021-11-01    0.993627\n",
       "2021-12-01    0.956218\n",
       "2022-01-01    0.943865\n",
       "2022-02-01    0.939786\n",
       "2022-03-01    0.938439\n",
       "2022-04-01    0.937995\n",
       "Freq: MS, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#预测\n",
    "print('未来7月的销量预测：')\n",
    "forecast = model.forecast(7) #预测、标准差、置信区间\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "sorted-yesterday",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '别克汽车销量预测')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.concat((sale,forecast),axis=0)\n",
    "data.columns=['销量','未来7月销量']\n",
    "data.index = pd.DatetimeIndex(data.index)\n",
    "data.plot()\n",
    "plt.title(\"别克汽车销量预测\") # 图形标题\n",
    "plt.rcParams['savefig.dpi'] = 300 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.savefig('sale.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "nasty-myanmar",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid=model.resid\n",
    "plt.rcParams['savefig.dpi'] = 100 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "#自相关图\n",
    "plot_acf(resid,lags=23).show()\n",
    "\n",
    "#解读：有短期相关性，但趋向于零。\n",
    "\n",
    "#偏自相关图\n",
    "plot_pacf(resid,lags=11).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "disturbed-consolidation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot(resid, line='q', fit=True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "lined-tsunami",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D-W检验的结果为： 1.8473042637081503\n"
     ]
    }
   ],
   "source": [
    "print('D-W检验的结果为：',sm.stats.durbin_watson(resid.values))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "reliable-spread",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "残差序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  0.037717   0.846013\n"
     ]
    }
   ],
   "source": [
    "# 方法一\n",
    "print('残差序列的白噪声检验结果为：',acorr_ljungbox(resid,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "published-aside",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
